Machine Learning Specialist

ML Engineer
Location: Pune | Hybrid
Experience: 8+ years
Primary Skill: Python, SQL, ML Modelling, ML Operations, Data science
Our Objective
We are building AI-powered solutions that help businesses improve customer outcomes, operational efficiency, revenue growth, and decision-making through the practical application of Machine Learning and AI.
As part of the AI Engineering team, you will work on the design, development, deployment, and optimization of ML-driven solutions that deliver measurable business value across customer-facing and operational workflows.
Key Responsibilities
Machine Learning Solution Development
Design, develop, and deploy Machine Learning models for prediction, recommendation, optimization, classification, and forecasting use cases.
Build scalable ML pipelines for data preparation, feature engineering, model training, evaluation, and deployment.
Apply statistical and machine learning techniques to solve business problems using structured and semi-structured data.
Work closely with product, engineering, and business teams to translate requirements into production-ready AI/ML solutions.
.
Data Engineering & Integration
Build and maintain data pipelines to ingest, transform, and process data from enterprise systems, APIs, databases, and external sources.
Develop reusable data services and ML components to accelerate solution delivery.
Ensure data quality, reliability, and scalability for model development and production workloads.
MLOps & Productionization
Implement CI/CD pipelines for ML models and AI services.
Establish model monitoring, performance tracking, retraining, and deployment processes.
Manage model lifecycle, experimentation, versioning, and governance.
Support deployment of AI and ML workloads on cloud platforms.
Engineering Excellence
Follow best practices for software engineering, testing, observability, and documentation.
Leverage AI-assisted development tools to improve engineering productivity.
Contribute to reusable frameworks, standards, and best practices across the AI team.
Required Qualifications
6+ years of software engineering experience with strong Python development skills.
5+ years of hands-on experience building and deploying Machine Learning solutions.
Strong understanding of supervised and unsupervised learning techniques.
Experience with recommendation systems, predictive analytics, forecasting, classification, anomaly detection, or optimization problems.
Hands-on experience with Scikit-Learn, XGBoost, LightGBM, TensorFlow, PyTorch, or equivalent ML frameworks.
Strong SQL and data analysis skills.
Experience with feature engineering, model evaluation, and experimentation frameworks.
Familiarity with MLOps practices, model deployment, monitoring, and lifecycle management.
Experience building data pipelines and integrating with enterprise systems through APIs and databases.
Experience with Docker, CI/CD pipelines, Git, and modern software engineering practices.
Experience working with AWS or Azure cloud platforms.
Strong analytical, problem-solving, and communication skills.
Good to Have
Advanced AI & Data Platforms
Experience with optimization techniques, routing algorithms, scheduling, or Operations Research.
Knowledge of demand forecasting, customer propensity modeling, pricing analytics, and recommendation engines.
Experience with explainable AI, model evaluation frameworks, and experimentation methodologies.
Data & Analytics
Knowledge of Operations Research, routing algorithms, scheduling, or decision optimization techniques.
Experience with demand forecasting, pricing analytics, customer intelligence, propensity modeling, and recommendation engines.
Experience with Snowflake, Databricks, or modern cloud data platforms.
Experience building analytical dashboards and decision-support solutions.
Familiarity with large-scale data processing and distributed computing.
Generative AI
Exposure to LLMs, RAG architectures, vector databases, and agentic frameworks.
Experience integrating ML solutions with GenAI applications.
Domain Knowledge
Exposure to sales, pricing, customer intelligence, e-commerce, distribution, logistics, supply chain, or ERP/CRM ecosystems.
Technology Stack
Languages: Python, SQL
ML Frameworks: Scikit-Learn, XGBoost, LightGBM, TensorFlow, PyTorch
Data: Snowflake, SQL, APIs, Data Pipelines
MLOps: MLflow, Docker, CI/CD, Model Monitoring
Cloud: AWS or Azure
Development Tools: GitHub, Azure DevOps, GitLab, GitHub Copilot, Cursor

Skills
Machine Learning, Python, Git, AWS, Azure, Docker, CI/CD, SQL, Snowflake, Databricks

Work arrangement
No